Visualize feature counts and variability

log10 = 0 is equale to a variance of 1 log10 = -2 is 0.01 log10 = -4
is 0.0001
ft <- gene_vars[gene_vars > 0]
quant <- quantile(gene_vars, 0.95)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
main = "Gene Variance (95% trimmed)", xlab = "Variance")

quant <- quantile(gene_vars, 0.90)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
main = "Gene Variance (90% trimmed)", xlab = "Variance")

quant <- quantile(gene_vars, 0.75)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
main = "Gene Variance (75% trimmed)", xlab = "Variance")

NA
NA
NA
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NA


ggplot(df, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_x_log10() +
scale_y_log10() +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()
ggplot(df, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()
ggplot(df, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_y_log10() +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()

ggplot(df_ft, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_x_log10() +
scale_y_log10() +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()

ggplot(df_ft, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()

ggplot(df_ft, aes(x = mean, y = variance)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_y_log10() +
labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
theme_minimal()

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NA

# cumulative variance plot
sorted_vars <- sort(gene_vars, decreasing = TRUE)
cumvar <- cumsum(sorted_vars) / sum(sorted_vars)
plot(cumvar, type = "l", lwd = 2, col = "darkgreen",
xlab = "Top N Genes", ylab = "Cumulative Variance Explained",
main = "Cumulative Variance Plot")

How “vst” variance colculaton works

Mean variance plot method


ggplot(df_ft, aes(x = mean, y = dispersion)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_x_log10() +
scale_y_log10() +
labs(x = "Log10 Mean Expression", y = "Log10 Dispersion") +
theme_minimal()
# Set your MVP thresholds
mean_cutoff <- c(0.1, 8)
dispersion_cutoff <- c(1, 50000) # Inf will not be plotted as a line
# Make the plot
ggplot(df_ft, aes(x = mean, y = dispersion)) +
geom_point(alpha = 0.3, color = "steelblue") +
scale_x_log10() +
scale_y_log10() +
# Add vertical lines for mean cutoff
geom_vline(xintercept = log10(mean_cutoff[1]), linetype = "dashed", color = "darkred") +
geom_vline(xintercept = log10(mean_cutoff[2]), linetype = "dashed", color = "darkred") +
# Add horizontal lines for dispersion cutoff (excluding Inf)
geom_hline(yintercept = log10(dispersion_cutoff[1]), linetype = "dashed", color = "darkgreen") +
labs(x = "Log10 Mean Expression", y = "Log10 Dispersion") +
theme_minimal()
ggplot(df_ft, aes(x = mean, y = dispersion)) +
geom_point(alpha = 0.3, color = "steelblue") +
# Add vertical lines for mean cutoff
geom_vline(xintercept = mean_cutoff[1], linetype = "dashed", color = "darkred") +
geom_vline(xintercept = mean_cutoff[2], linetype = "dashed", color = "darkred") +
# Add horizontal lines for dispersion cutoff (excluding Inf)
geom_hline(yintercept = dispersion_cutoff[1], linetype = "dashed", color = "darkgreen") +
labs(x = "Mean Expression", y = "Dispersion") +
theme_minimal()

NA
NA
Plot dispersion

Explore Variable Feature Selection Overlap
seu <- FindVariableFeatures(seu, selection.method = "dispersion",
loess.span = 0.3,
clip.max = "auto",
num.bin = 20,
nfeatures = 2000,
binning.method = "equal_width",
mean.cutoff = c(0.1, 8),
dispersion.cutoff = c(1, Inf),
verbose = TRUE)
G3;Finding variable features for layer data
gCalculating gene means
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variance to mean ratios
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Var_ft_dis <- VariableFeatures(seu)
seu@misc$Var_ft_dis <- Var_ft_dis
VariableFeatures(seu) <- seu@misc$Var_ft_dis
# Identify the 10 most highly variable genes
top10dis <- head(VariableFeatures(seu), 10)
# plot variable features with and without labels
plot3 <- VariableFeaturePlot(seu, assay = "RNA", selection.method = "dispersion",top10dis, repel = TRUE)
G1;H1;Errorh in VariableFeaturePlot(seu, assay = "RNA", selection.method = "dispersion", :
unused argument (repel = TRUE)
Error during wrapup: not that many frames on the stack
Error: no more error handlers available (recursive errors?); invoking 'abort' restart
g
LabelPoints(plot = plot2, points = top10dis, repel = TRUE)
G3;When using repel, set xnudge and ynudge to 0 for optimal results
gG2;H2;Warningh: Removed 2 rows containing missing values or values outside the scale range (`geom_point()`).g

---
title: "R Notebook"
output: html_notebook
---

Bonus visualization or comparisons

```{r}
rm(list = ls())

library(Seurat)
library(ggplot2)
library(patchwork)
library(dplyr)
library(Matrix)
library(UpSetR)

# your data or any example data can be used

# seu <- readRDS("SeuratObject.rds")
seu

```






# Visualize feature counts and variability
```{r}

# Compute variance of all genes manually (if not using Seurat's variable features)

expr_mat <- GetAssayData(seu, assay = "RNA", layer = "counts")
expr_mat[1:8,1:5]
# Step 1: Get means
gene_means <- rowMeans(expr_mat)

# Step 2: Compute squared deviations and mean
gene_vars <- as.numeric(rowMeans(expr_mat^2) - gene_means^2)
names(gene_vars) <- rownames(expr_mat)
gene_vars[1:10]

df <- data.frame(gene = names(gene_vars), variance = gene_vars)

# Histogram
hist(df$variance, breaks = 100, col = "steelblue",
     main = "Distribution of Gene Variance",
     xlab = "Variance")

summary(gene_vars)



hist(log10(gene_vars[gene_vars > 0]), breaks = 100, col = "tomato",
     main = "Log10 Gene Variance", xlab = "log10(Variance)")


# with pseudo counts
log_gene_vars <- log10(gene_vars + 1e-6)
hist(log_gene_vars, breaks = 100, col = "darkred",
     main = "Log10 Gene Variance", xlab = "log10(Variance)")



```
log10 = 0 is equale to a variance of 1
log10 = -2 is 0.01
log10 = -4 is 0.0001




```{r}


ft <- gene_vars[gene_vars > 0]
quant <- quantile(gene_vars, 0.95)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
     main = "Gene Variance (95% trimmed)", xlab = "Variance")

quant <- quantile(gene_vars, 0.90)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
     main = "Gene Variance (90% trimmed)", xlab = "Variance")


quant <- quantile(gene_vars, 0.75)
hist(ft[ft < quant], breaks = 100, col = "steelblue",
     main = "Gene Variance (75% trimmed)", xlab = "Variance")





```

```{r}
library(ggplot2)
plus_gene_vars = gene_vars + 0.0000000001


df <- data.frame(
  gene = rownames(expr_mat),
  mean = gene_means,
  variance = plus_gene_vars
)

df_ft <- df[df$variance > 0.00001 ,]

ggplot(df, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_x_log10() +
  scale_y_log10() +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()


ggplot(df, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()


ggplot(df, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_y_log10() +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()



ggplot(df_ft, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_x_log10() +
  scale_y_log10() +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()


ggplot(df_ft, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()


ggplot(df_ft, aes(x = mean, y = variance)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_y_log10() +
  labs(title = "Gene Dispersion", x = "Mean Expression", y = "Variance") +
  theme_minimal()




```







```{r}
# larger variance 

quant <- quantile(gene_vars, 0.75)
large_var <- gene_vars[gene_vars > quant]
summary(large_var)

hist(large_var[large_var < 3], breaks = 100, col = "steelblue",
     main = "Gene Variance above 75% percentile and lower than 3", xlab = "Variance")



quant <- quantile(gene_vars, 0.95)
large_var <- gene_vars[gene_vars > quant]
summary(large_var)
hist(large_var[large_var < 200], breaks = 100, col = "steelblue",
     main = "Gene Variance above 95% percentile and lower than 200", xlab = "Variance")



quant <- quantile(gene_vars, 0.99)
large_var <- gene_vars[gene_vars > quant]
summary(large_var)
hist(large_var[large_var < 3000], breaks = 100, col = "steelblue",
     main = "Gene Variance above 99% percentile ", xlab = "Variance")



```







```{r}
# cumulative variance plot
sorted_vars <- sort(gene_vars, decreasing = TRUE)
cumvar <- cumsum(sorted_vars) / sum(sorted_vars)

plot(cumvar, type = "l", lwd = 2, col = "darkgreen",
     xlab = "Top N Genes", ylab = "Cumulative Variance Explained",
     main = "Cumulative Variance Plot")
```

How "vst" variance colculaton works

```{r}
# expr_mat was taken above
means <- rowMeans(expr_mat)
vars <- as.numeric(rowMeans(expr_mat^2) - gene_means^2)


# Step 2: Log transform
log_means <- log10(means)  # 
log_vars <- log10(vars)

# Step 3: Fit loess curve
fit <- loess(log_vars ~ log_means)

# Step 4: Predict expected variance
expected_log_vars <- predict(fit)

# Step 5: Plot
plot(log_means, log_vars, pch = 16, col = rgb(0.2, 0.4, 0.8, 0.3),
     xlab = "log10(Mean)", ylab = "log10(Expected Variance)", main = "Mean-Variance with LOESS Fit")
lines(log_means[order(log_means)], expected_log_vars[order(log_means)],
      col = "red", lwd = 2)

```

Mean variance plot method

```{r}

# 3. Compute "dispersion" (variance / mean)
dispersion <- (gene_vars + 0.00000001) / gene_means

df <- data.frame(
  gene = rownames(expr_mat),
  mean = gene_means,
  variance = gene_vars,
  dispersion = dispersion
)

df_ft <- df[!is.nan(df$dispersion), ]
df_ft <- df[is.finite(df$dispersion) & !is.na(df$dispersion), ]



ggplot(df_ft, aes(x = mean, y = dispersion)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_x_log10() +
  scale_y_log10() +
  labs(x = "Log10 Mean Expression", y = "Log10 Dispersion") +
  theme_minimal()


# Set your MVP thresholds
mean_cutoff <- c(0.1, 8)
dispersion_cutoff <- c(1, 50000)  # Inf will not be plotted as a line

# Make the plot
ggplot(df_ft, aes(x = mean, y = dispersion)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_x_log10() +
  scale_y_log10() +
  # Add vertical lines for mean cutoff
  geom_vline(xintercept = log10(mean_cutoff[1]), linetype = "dashed", color = "darkred") +
    geom_vline(xintercept = log10(mean_cutoff[2]), linetype = "dashed", color = "darkred") +
  # Add horizontal lines for dispersion cutoff (excluding Inf)
  geom_hline(yintercept = log10(dispersion_cutoff[1]), linetype = "dashed", color = "darkgreen") +
  labs(x = "Log10 Mean Expression", y = "Log10 Dispersion") +
  theme_minimal()



ggplot(df_ft, aes(x = mean, y = dispersion)) +
  geom_point(alpha = 0.3, color = "steelblue") +

  # Add vertical lines for mean cutoff
  geom_vline(xintercept = mean_cutoff[1], linetype = "dashed", color = "darkred") +
    geom_vline(xintercept = mean_cutoff[2], linetype = "dashed", color = "darkred") +
  # Add horizontal lines for dispersion cutoff (excluding Inf)
  geom_hline(yintercept = dispersion_cutoff[1], linetype = "dashed", color = "darkgreen") +
  labs(x = "Mean Expression", y = "Dispersion") +
  theme_minimal()




```

Plot dispersion 

```{r}

# Bin genes by mean expression
df_bin <- df_ft %>%
  mutate(bin = ntile(mean, 20)) %>%  # 20 bins like Seurat
  group_by(bin) %>%
  mutate(scaled_dispersion = scale(dispersion)[,1]) %>%
  ungroup()

# Plot: log10 mean vs. scaled dispersion
ggplot(df_bin, aes(x = mean, y = scaled_dispersion)) +
  geom_point(alpha = 0.3, color = "steelblue") +
  scale_x_log10() +
  labs(
    x = "Log10 Mean Expression",
    y = "Scaled Dispersion (Z-score)",
    title = "Dispersion Method (Seurat)"
  ) +
  theme_minimal()


```


Explore Variable Feature Selection Overlap

```{r}



# calculate variable features in different ways
FindVariableFeatures(seu, selection.method = "vst",
  loess.span = 0.3,
  clip.max = "auto",
  num.bin = 20,
  binning.method = "equal_width",
  nfeatures = 2000, # default
  mean.cutoff = c(0.1, 8), # used in mvp
  dispersion.cutoff = c(1, Inf), # used in dispression
  verbose = TRUE)


Var_ft_vst <- VariableFeatures(seu)
# put the features into a spot where we can recover them later
seu@misc$Var_ft_vst <- Var_ft_vst

top10vst <- head(seu@misc$Var_ft_vst, 10)
plot1 <- VariableFeaturePlot(seu, assay = "RNA", selection.method = "vst") 
LabelPoints(plot = plot1, points = top10vst, repel = TRUE)


cat("---------------------------------------------------------------------------------------\n\n")

seu <- FindVariableFeatures(seu, selection.method = "mean.var.plot",
  loess.span = 0.3,
  clip.max = "auto",
  num.bin = 20,
  binning.method = "equal_width",
  mean.cutoff = c(0.1, 8),
  dispersion.cutoff = c(1, Inf),
  verbose = TRUE)

Var_ft_mvp <- VariableFeatures(seu)
seu@misc$Var_ft_mvp <- Var_ft_mvp

VariableFeatures(seu) <- seu@misc$Var_ft_mvp
# Identify the 10 most highly variable genes
top10mvp <- head(seu@misc$Var_ft_mvp, 10)

# plot variable features with and without labels
plot2 <- VariableFeaturePlot(seu, assay = "RNA", selection.method = "mean.var.plot")  
LabelPoints(plot = plot2, points = top10mvp, repel = TRUE)

cat("---------------------------------------------------------------------------------------\n\n")


seu <- FindVariableFeatures(seu, selection.method = "dispersion",
  loess.span = 0.3,
  clip.max = "auto",
  num.bin = 20, 
   nfeatures = 2000,
  binning.method = "equal_width",
  mean.cutoff = c(0.1, 8),
  dispersion.cutoff = c(1, Inf),
  verbose = TRUE)

Var_ft_dis <- VariableFeatures(seu)
seu@misc$Var_ft_dis <- Var_ft_dis


VariableFeatures(seu) <- seu@misc$Var_ft_dis
# Identify the 10 most highly variable genes
top10dis <- head(VariableFeatures(seu), 10)

# plot variable features with and without labels
plot3 <- VariableFeaturePlot(seu, assay = "RNA", selection.method = "dispersion",top10dis, repel = TRUE)
LabelPoints(plot = plot2, points = top10dis, repel = TRUE)



cat("---------------------------------------------------------------------------------------\n\n")




```



# make an upset plot comparing the 3 FindVariableFeatures options
```{r}


upset(fromList(list(vst = Var_ft_vst, mvp = Var_ft_mvp, disp = Var_ft_dis)))

# more options gene lists can be included if wanted

```



Make an upset plot for the variable features for each sample and the merged object

```{r}

# example
upset(fromList(list(C1 = Var_C1, C2 = Var_C2, C3 = Var_C3, C4 = Var_C4, Combined = Var_obj)))

```









